Class FeatureAgglomerationScikitsLearnNode

Agglomerate features.
This node has been automatically generated by wrapping the ``sklearn.cluster.hierarchical.FeatureAgglomeration`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
Similar to AgglomerativeClustering, but recursively merges features
instead of samples.
Read more in the :ref:`User Guide <hierarchical_clustering>`.
**Parameters**
n_clusters : int, default 2
The number of clusters to find.
connectivity : array-like or callable, optional
Connectivity matrix. Defines for each feature the neighboring
features following a given structure of the data.
This can be a connectivity matrix itself or a callable that transforms
the data into a connectivity matrix, such as derived from
kneighbors_graph. Default is None, i.e, the
hierarchical clustering algorithm is unstructured.
affinity : string or callable, default "euclidean"
Metric used to compute the linkage. Can be "euclidean", "l1", "l2",
"manhattan", "cosine", or 'precomputed'.
If linkage is "ward", only "euclidean" is accepted.
memory : Instance of joblib.Memory or string, optional
Used to cache the output of the computation of the tree.
By default, no caching is done. If a string is given, it is the
path to the caching directory.
n_components : int (optional)
Number of connected components. If None the number of connected
components is estimated from the connectivity matrix.
NOTE: This parameter is now directly determined from the connectivity
matrix and will be removed in 0.18
compute_full_tree : bool or 'auto', optional, default "auto"
Stop early the construction of the tree at n_clusters. This is
useful to decrease computation time if the number of clusters is
not small compared to the number of features. This option is
useful only when specifying a connectivity matrix. Note also that
when varying the number of clusters and using caching, it may
be advantageous to compute the full tree.
linkage : {"ward", "complete", "average"}, optional, default "ward"
Which linkage criterion to use. The linkage criterion determines which
distance to use between sets of features. The algorithm will merge
the pairs of cluster that minimize this criterion.
- ward minimizes the variance of the clusters being merged.
- average uses the average of the distances of each feature of
the two sets.
- complete or maximum linkage uses the maximum distances between
all features of the two sets.
pooling_func : callable, default np.mean
This combines the values of agglomerated features into a single
value, and should accept an array of shape [M, N] and the keyword
argument `axis=1`, and reduce it to an array of size [M].
**Attributes**
``labels_`` : array-like, (n_features,)
cluster labels for each feature.
``n_leaves_`` : int
Number of leaves in the hierarchical tree.
``n_components_`` : int
The estimated number of connected components in the graph.
``children_`` : array-like, shape (n_nodes-1, 2)
The children of each non-leaf node. Values less than `n_features`
correspond to leaves of the tree which are the original samples.
A node `i` greater than or equal to `n_features` is a non-leaf
node and has children `children_[i - n_features]`. Alternatively
at the i-th iteration, children[i][0] and children[i][1]
are merged to form node `n_features + i`

Agglomerate features.
This node has been automatically generated by wrapping the ``sklearn.cluster.hierarchical.FeatureAgglomeration`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
Similar to AgglomerativeClustering, but recursively merges features
instead of samples.
Read more in the :ref:`User Guide <hierarchical_clustering>`.
**Parameters**
n_clusters : int, default 2
The number of clusters to find.
connectivity : array-like or callable, optional
Connectivity matrix. Defines for each feature the neighboring
features following a given structure of the data.
This can be a connectivity matrix itself or a callable that transforms
the data into a connectivity matrix, such as derived from
kneighbors_graph. Default is None, i.e, the
hierarchical clustering algorithm is unstructured.
affinity : string or callable, default "euclidean"
Metric used to compute the linkage. Can be "euclidean", "l1", "l2",
"manhattan", "cosine", or 'precomputed'.
If linkage is "ward", only "euclidean" is accepted.
memory : Instance of joblib.Memory or string, optional
Used to cache the output of the computation of the tree.
By default, no caching is done. If a string is given, it is the
path to the caching directory.
n_components : int (optional)
Number of connected components. If None the number of connected
components is estimated from the connectivity matrix.
NOTE: This parameter is now directly determined from the connectivity
matrix and will be removed in 0.18
compute_full_tree : bool or 'auto', optional, default "auto"
Stop early the construction of the tree at n_clusters. This is
useful to decrease computation time if the number of clusters is
not small compared to the number of features. This option is
useful only when specifying a connectivity matrix. Note also that
when varying the number of clusters and using caching, it may
be advantageous to compute the full tree.
linkage : {"ward", "complete", "average"}, optional, default "ward"
Which linkage criterion to use. The linkage criterion determines which
distance to use between sets of features. The algorithm will merge
the pairs of cluster that minimize this criterion.
- ward minimizes the variance of the clusters being merged.
- average uses the average of the distances of each feature of
the two sets.
- complete or maximum linkage uses the maximum distances between
all features of the two sets.
pooling_func : callable, default np.mean
This combines the values of agglomerated features into a single
value, and should accept an array of shape [M, N] and the keyword
argument `axis=1`, and reduce it to an array of size [M].
**Attributes**
``labels_`` : array-like, (n_features,)
cluster labels for each feature.
``n_leaves_`` : int
Number of leaves in the hierarchical tree.
``n_components_`` : int
The estimated number of connected components in the graph.
``children_`` : array-like, shape (n_nodes-1, 2)
The children of each non-leaf node. Values less than `n_features`
correspond to leaves of the tree which are the original samples.
A node `i` greater than or equal to `n_features` is a non-leaf
node and has children `children_[i - n_features]`. Alternatively
at the i-th iteration, children[i][0] and children[i][1]
are merged to form node `n_features + i`

_stop_training(self,
**kwargs)

execute(self,
x)

Transform a new matrix using the built clustering

This node has been automatically generated by wrapping the sklearn.cluster.hierarchical.FeatureAgglomeration class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.

Parameters

X :array-like, shape = [n_samples, n_features] or [n_features]

A M by N array of M observations in N dimensions or a length
M array of M one-dimensional observations.

pooling_func :callable, default=np.mean

This combines the values of agglomerated features into a single
value, and should accept an array of shape [M, N] and the keyword
argument axis=1, and reduce it to an array of size [M].

is_trainable()Static Method

stop_training(self,
**kwargs)

Fit the hierarchical clustering on the data

This node has been automatically generated by wrapping the sklearn.cluster.hierarchical.FeatureAgglomeration class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.